OpenAI GPT-OSS 120B vs Qwen2.5 7B Instruct Turbo
On provider list prices, Qwen2.5 7B Instruct Turbo costs $0.30 per million input tokens against $0.15 for OpenAI GPT-OSS 120B: effectively level. Output is $0.30 against $0.60 (2.0x). On Allocate both bill at list plus the 7% transaction fee.
Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.
What the numbers say
Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $390 a month on OpenAI GPT-OSS 120B and $465 on Qwen2.5 7B Instruct Turbo at list: a gap of $75, or 1.2x.
OpenAI GPT-OSS 120B reads 128K tokens per request against 32K for Qwen2.5 7B Instruct Turbo, 4.0x the window. That decides which one can take whole documents without splitting them.
Choose OpenAI GPT-OSS 120B for
- The lower list price ($0.15 in / $0.60 out per M tokens)
- The longer context window (128K vs 32K tokens)
Choose Qwen2.5 7B Instruct Turbo for
- Training toward a model you own
Common questions
Which is cheaper, OpenAI GPT-OSS 120B or Qwen2.5 7B Instruct Turbo?
OpenAI GPT-OSS 120B, on this workload shape. At list prices it is $0.15/$0.60 per million tokens in and out against $0.30/$0.30 for Qwen2.5 7B Instruct Turbo. Billed on Allocate: $0.16/$0.64 against $0.32/$0.32, list plus 7%.
Which has the bigger context window?
OpenAI GPT-OSS 120B: 131,072 tokens (128K) against 32,768 (32K) for Qwen2.5 7B Instruct Turbo.
Can I fine-tune OpenAI GPT-OSS 120B or Qwen2.5 7B Instruct Turbo?
Both publish open weights (OpenAI GPT-OSS 120B: Custom license; Qwen2.5 7B Instruct Turbo: Qwen license), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.
Related comparisons
Run the numbers on your workload
Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.